{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Exploratory"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>f38a6374c348f90b587e046aac6079959adf3835</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>c18f2d887b7ae4f6742ee445113fa1aef383ed77</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>755db6279dae599ebb4d39a9123cce439965282d</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>bc3f0c64fb968ff4a8bd33af6971ecae77c75e08</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>068aba587a4950175d04c680d38943fd488d6a9d</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                         id  label\n",
       "0  f38a6374c348f90b587e046aac6079959adf3835      0\n",
       "1  c18f2d887b7ae4f6742ee445113fa1aef383ed77      1\n",
       "2  755db6279dae599ebb4d39a9123cce439965282d      0\n",
       "3  bc3f0c64fb968ff4a8bd33af6971ecae77c75e08      0\n",
       "4  068aba587a4950175d04c680d38943fd488d6a9d      0"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd \n",
    "\n",
    "path2csv=\"./data/train_labels.csv\"\n",
    "labels_df=pd.read_csv(path2csv)\n",
    "labels_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0    130908\n",
      "1     89117\n",
      "Name: label, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(labels_df['label'].value_counts())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "labels_df['label'].hist();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 9 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pylab as plt\n",
    "from PIL import Image, ImageDraw\n",
    "import numpy as np\n",
    "import os\n",
    "%matplotlib inline\n",
    "\n",
    "# data is stored here\n",
    "path2train=\"./data/train/\"\n",
    "\n",
    "# show images in gray-scale, if you want color change it to True\n",
    "color=False\n",
    "\n",
    "# get ids for malignant images\n",
    "malignantIds = labels_df.loc[labels_df['label']==1]['id'].values\n",
    "\n",
    "plt.rcParams['figure.figsize'] = (10.0, 10.0)\n",
    "plt.subplots_adjust(wspace=0, hspace=0)\n",
    "nrows,ncols=3,3\n",
    "for i,id_ in enumerate(malignantIds[:nrows*ncols]):\n",
    "    full_filenames = os.path.join(path2train , id_ +'.tif')\n",
    " \n",
    "    # load image\n",
    "    img = Image.open(full_filenames)\n",
    "\n",
    "    # draw a 32*32 rectangle \n",
    "    draw = ImageDraw.Draw(img)\n",
    "    draw.rectangle(((32, 32), (64, 64)),outline=\"green\")\n",
    "\n",
    "    plt.subplot(nrows, ncols, i+1) \n",
    "    if color is True:\n",
    "        plt.imshow(np.array(img))\n",
    "    else:\n",
    "        plt.imshow(np.array(img)[:,:,0],cmap=\"gray\")\n",
    "    plt.axis('off')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "image shape: (96, 96, 3)\n",
      "pixel values range from 0 to 255\n"
     ]
    }
   ],
   "source": [
    "print(\"image shape:\", np.array(img).shape)\n",
    "print(\"pixel values range from %s to %s\" %(np.min(img), np.max(img)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Create a Custom Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "from PIL import Image\n",
    "from torch.utils.data import Dataset\n",
    "import pandas as pd\n",
    "import torchvision.transforms as transforms\n",
    "import os\n",
    "\n",
    "# fix torch random seed\n",
    "torch.manual_seed(0)\n",
    "\n",
    "class histoCancerDataset(Dataset):\n",
    "    def __init__(self, data_dir, transform,data_type=\"train\"):      \n",
    "    \n",
    "        # path to images\n",
    "        path2data=os.path.join(data_dir,data_type)\n",
    "\n",
    "        # get list of images\n",
    "        filenames = os.listdir(path2data)\n",
    "\n",
    "        # get the full path to images\n",
    "        self.full_filenames = [os.path.join(path2data, f) for f in filenames]\n",
    "\n",
    "        # labels are in a csv file named train_labels.csv\n",
    "        path2csvLabels=os.path.join(data_dir,\"train_labels.csv\")\n",
    "        labels_df=pd.read_csv(path2csvLabels)\n",
    "\n",
    "        # set data frame index to id\n",
    "        labels_df.set_index(\"id\", inplace=True)\n",
    "\n",
    "        # obtain labels from data frame\n",
    "        self.labels = [labels_df.loc[filename[:-4]].values[0] for filename in filenames]\n",
    "\n",
    "        self.transform = transform\n",
    "      \n",
    "    def __len__(self):\n",
    "        # return size of dataset\n",
    "        return len(self.full_filenames)\n",
    "      \n",
    "    def __getitem__(self, idx):\n",
    "        # open image, apply transforms and return with label\n",
    "        image = Image.open(self.full_filenames[idx])  # PIL image\n",
    "        image = self.transform(image)\n",
    "        return image, self.labels[idx]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torchvision.transforms as transforms\n",
    "data_transformer = transforms.Compose([transforms.ToTensor()])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "220025\n"
     ]
    }
   ],
   "source": [
    "data_dir = \"./data/\"\n",
    "histo_dataset = histoCancerDataset(data_dir, data_transformer, \"train\")\n",
    "print(len(histo_dataset))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([3, 96, 96]) tensor(0.) tensor(1.)\n"
     ]
    }
   ],
   "source": [
    "# load an image\n",
    "img,label=histo_dataset[9]\n",
    "print(img.shape,torch.min(img),torch.max(img))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Split Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "train dataset length: 176020\n",
      "validation dataset length: 44005\n"
     ]
    }
   ],
   "source": [
    "from torch.utils.data import random_split\n",
    "\n",
    "len_histo=len(histo_dataset)\n",
    "len_train=int(0.8*len_histo)\n",
    "len_val=len_histo-len_train\n",
    "\n",
    "train_ds,val_ds=random_split(histo_dataset,[len_train,len_val])\n",
    "\n",
    "print(\"train dataset length:\", len(train_ds))\n",
    "print(\"validation dataset length:\", len(val_ds))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([3, 96, 96]) 0\n"
     ]
    }
   ],
   "source": [
    "for x,y in train_ds:\n",
    "    print(x.shape,y)\n",
    "    break"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([3, 96, 96]) 0\n"
     ]
    }
   ],
   "source": [
    "for x,y in val_ds:\n",
    "    print(x.shape,y)\n",
    "    break"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "image indices: [ 43567 173685 117952 152315]\n",
      "torch.Size([3, 100, 394])\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from torchvision import utils\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "np.random.seed(0)\n",
    "\n",
    "\n",
    "def show(img,y,color=False):\n",
    "    # convert tensor to numpy array\n",
    "    npimg = img.numpy()\n",
    "   \n",
    "    # Convert to H*W*C shape\n",
    "    npimg_tr=np.transpose(npimg, (1,2,0))\n",
    "    \n",
    "    if color==False:\n",
    "        npimg_tr=npimg_tr[:,:,0]\n",
    "        plt.imshow(npimg_tr,interpolation='nearest',cmap=\"gray\")\n",
    "    else:\n",
    "        # display images\n",
    "        plt.imshow(npimg_tr,interpolation='nearest')\n",
    "    plt.title(\"label: \"+str(y))\n",
    "\n",
    "grid_size=4\n",
    "rnd_inds=np.random.randint(0,len(train_ds),grid_size)\n",
    "print(\"image indices:\",rnd_inds)\n",
    "\n",
    "x_grid_train=[train_ds[i][0] for i in rnd_inds]\n",
    "y_grid_train=[train_ds[i][1] for i in rnd_inds]\n",
    "\n",
    "x_grid_train=utils.make_grid(x_grid_train, nrow=4, padding=2)\n",
    "print(x_grid_train.shape)\n",
    "\n",
    "plt.rcParams['figure.figsize'] = (10.0, 5)\n",
    "show(x_grid_train,y_grid_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "image indices: [30403 32103 41993 20757]\n",
      "torch.Size([3, 100, 394])\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "grid_size=4\n",
    "rnd_inds=np.random.randint(0,len(val_ds),grid_size)\n",
    "print(\"image indices:\",rnd_inds)\n",
    "x_grid_val=[val_ds[i][0] for i in range(grid_size)]\n",
    "y_grid_val=[val_ds[i][1] for i in range(grid_size)]\n",
    "\n",
    "x_grid_val=utils.make_grid(x_grid_val, nrow=4, padding=2)\n",
    "print(x_grid_val.shape)\n",
    "\n",
    "show(x_grid_val,y_grid_val)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Transform Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_transformer = transforms.Compose([\n",
    "    transforms.RandomHorizontalFlip(p=0.5),  \n",
    "    transforms.RandomVerticalFlip(p=0.5),  \n",
    "    transforms.RandomRotation(45),         \n",
    "    transforms.RandomResizedCrop(96,scale=(0.8,1.0),ratio=(1.0,1.0)),\n",
    "    transforms.ToTensor()])             "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "val_transformer = transforms.Compose([transforms.ToTensor()])     "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "# overwrite the transform functions\n",
    "train_ds.transform=train_transformer\n",
    "val_ds.transform=val_transformer"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Create Dataloader"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "from torch.utils.data import DataLoader\n",
    "train_dl = DataLoader(train_ds, batch_size=32, shuffle=True)\n",
    "val_dl = DataLoader(val_ds, batch_size=64, shuffle=False)  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([32, 3, 96, 96])\n",
      "torch.Size([32])\n"
     ]
    }
   ],
   "source": [
    "# extract a batch from trainin data\n",
    "for x, y in train_dl:\n",
    "    print(x.shape)\n",
    "    print(y.shape)\n",
    "    break\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([64, 3, 96, 96])\n",
      "torch.Size([64])\n"
     ]
    }
   ],
   "source": [
    "# extract a batch from validation data\n",
    "for x, y in val_dl:\n",
    "    print(x.shape)\n",
    "    print(y.shape)\n",
    "    break"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Building Classification Model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## dumb baselines "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "accuracy random prediction: 0.50\n",
      "accuracy all zero prediction: 0.60\n",
      "accuracy all one prediction: 0.40\n"
     ]
    }
   ],
   "source": [
    "# get labels for validation dataset\n",
    "y_val=[y for _,y in val_ds]    \n",
    "\n",
    "def accuracy(labels, out):\n",
    "    return np.sum(out==labels)/float(len(labels))\n",
    "\n",
    "# accuracy all zero predictions\n",
    "acc_all_zeros=accuracy(y_val,np.zeros_like(y_val))\n",
    "\n",
    "# accuracy all ones predictions\n",
    "acc_all_ones=accuracy(y_val,np.ones_like(y_val))\n",
    "\n",
    "# accuracy random predictions\n",
    "acc_random=accuracy(y_val,np.random.randint(2,size=len(y_val)))\n",
    "\n",
    "print(\"accuracy random prediction: %.2f\" %acc_random)\n",
    "print(\"accuracy all zero prediction: %.2f\" %acc_all_zeros)\n",
    "print(\"accuracy all one prediction: %.2f\" %acc_all_ones)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## find Output size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "47 47\n"
     ]
    }
   ],
   "source": [
    "import torch.nn as nn\n",
    "\n",
    "def findConv2dOutShape(H_in,W_in,conv,pool=2):\n",
    "    # get conv arguments\n",
    "    kernel_size=conv.kernel_size\n",
    "    stride=conv.stride\n",
    "    padding=conv.padding\n",
    "    dilation=conv.dilation\n",
    "\n",
    "    # Ref: https://pytorch.org/docs/stable/nn.html\n",
    "    H_out=np.floor((H_in+2*padding[0]-dilation[0]*(kernel_size[0]-1)-1)/stride[0]+1)\n",
    "    W_out=np.floor((W_in+2*padding[1]-dilation[1]*(kernel_size[1]-1)-1)/stride[1]+1)\n",
    "\n",
    "    if pool:\n",
    "        H_out/=pool\n",
    "        W_out/=pool\n",
    "    return int(H_out),int(W_out)\n",
    "\n",
    "\n",
    "# example\n",
    "conv1 = nn.Conv2d(3, 8, kernel_size=3)\n",
    "h,w=findConv2dOutShape(96,96,conv1)\n",
    "print(h,w)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Define Model "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch.nn as nn\n",
    "import torch.nn.functional as F\n",
    "\n",
    "class Net(nn.Module):\n",
    "    def __init__(self, params):\n",
    "        super(Net, self).__init__()\n",
    "    \n",
    "        C_in,H_in,W_in=params[\"input_shape\"]\n",
    "        init_f=params[\"initial_filters\"] \n",
    "        num_fc1=params[\"num_fc1\"]  \n",
    "        num_classes=params[\"num_classes\"] \n",
    "        self.dropout_rate=params[\"dropout_rate\"] \n",
    "        \n",
    "        self.conv1 = nn.Conv2d(C_in, init_f, kernel_size=3)\n",
    "        h,w=findConv2dOutShape(H_in,W_in,self.conv1)\n",
    "        \n",
    "        self.conv2 = nn.Conv2d(init_f, 2*init_f, kernel_size=3)\n",
    "        h,w=findConv2dOutShape(h,w,self.conv2)\n",
    "        \n",
    "        self.conv3 = nn.Conv2d(2*init_f, 4*init_f, kernel_size=3)\n",
    "        h,w=findConv2dOutShape(h,w,self.conv3)\n",
    "\n",
    "        self.conv4 = nn.Conv2d(4*init_f, 8*init_f, kernel_size=3)\n",
    "        h,w=findConv2dOutShape(h,w,self.conv4)\n",
    "        \n",
    "        # compute the flatten size\n",
    "        self.num_flatten=h*w*8*init_f\n",
    "        \n",
    "        self.fc1 = nn.Linear(self.num_flatten, num_fc1)\n",
    "        self.fc2 = nn.Linear(num_fc1, num_classes)\n",
    "\n",
    "    def forward(self, x):\n",
    "        x = F.relu(self.conv1(x))\n",
    "        x = F.max_pool2d(x, 2, 2)\n",
    "        \n",
    "        x = F.relu(self.conv2(x))\n",
    "        x = F.max_pool2d(x, 2, 2)\n",
    "\n",
    "        x = F.relu(self.conv3(x))\n",
    "        x = F.max_pool2d(x, 2, 2)\n",
    "\n",
    "        x = F.relu(self.conv4(x))\n",
    "        x = F.max_pool2d(x, 2, 2)\n",
    "        \n",
    "        x = x.view(-1, self.num_flatten)\n",
    "        \n",
    "        x = F.relu(self.fc1(x))\n",
    "        x=F.dropout(x, self.dropout_rate)\n",
    "        x = self.fc2(x)\n",
    "        return F.log_softmax(x, dim=1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "# dict to define model parameters\n",
    "params_model={\n",
    "        \"input_shape\": (3,96,96),\n",
    "        \"initial_filters\": 8,    \n",
    "        \"num_fc1\": 100,\n",
    "        \"dropout_rate\": 0.25,\n",
    "        \"num_classes\": 2,\n",
    "            }\n",
    "\n",
    "# create model\n",
    "cnn_model = Net(params_model)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "# move model to cuda/gpu device\n",
    "if torch.cuda.is_available():\n",
    "    device = torch.device(\"cuda\")\n",
    "    cnn_model=cnn_model.to(device) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Net(\n",
      "  (conv1): Conv2d(3, 8, kernel_size=(3, 3), stride=(1, 1))\n",
      "  (conv2): Conv2d(8, 16, kernel_size=(3, 3), stride=(1, 1))\n",
      "  (conv3): Conv2d(16, 32, kernel_size=(3, 3), stride=(1, 1))\n",
      "  (conv4): Conv2d(32, 64, kernel_size=(3, 3), stride=(1, 1))\n",
      "  (fc1): Linear(in_features=1024, out_features=100, bias=True)\n",
      "  (fc2): Linear(in_features=100, out_features=2, bias=True)\n",
      ")\n"
     ]
    }
   ],
   "source": [
    "print(cnn_model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cuda:0\n"
     ]
    }
   ],
   "source": [
    "print(next(cnn_model.parameters()).device)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "----------------------------------------------------------------\n",
      "        Layer (type)               Output Shape         Param #\n",
      "================================================================\n",
      "            Conv2d-1            [-1, 8, 94, 94]             224\n",
      "            Conv2d-2           [-1, 16, 45, 45]           1,168\n",
      "            Conv2d-3           [-1, 32, 20, 20]           4,640\n",
      "            Conv2d-4             [-1, 64, 8, 8]          18,496\n",
      "            Linear-5                  [-1, 100]         102,500\n",
      "            Linear-6                    [-1, 2]             202\n",
      "================================================================\n",
      "Total params: 127,230\n",
      "Trainable params: 127,230\n",
      "Non-trainable params: 0\n",
      "----------------------------------------------------------------\n",
      "Input size (MB): 0.11\n",
      "Forward/backward pass size (MB): 0.92\n",
      "Params size (MB): 0.49\n",
      "Estimated Total Size (MB): 1.51\n",
      "----------------------------------------------------------------\n"
     ]
    }
   ],
   "source": [
    "from torchsummary import summary\n",
    "summary(cnn_model, input_size=(3, 96, 96))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Loss function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "loss_func = nn.NLLLoss(reduction=\"sum\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([8, 2])\n",
      "torch.Size([8])\n",
      "5.266995429992676\n"
     ]
    }
   ],
   "source": [
    "# fixed random seed\n",
    "torch.manual_seed(0)\n",
    "\n",
    "n,c=8,2\n",
    "y = torch.randn(n, c, requires_grad=True)\n",
    "ls_F = nn.LogSoftmax(dim=1)\n",
    "y_out=ls_F(y)\n",
    "print(y_out.shape)\n",
    "\n",
    "target = torch.randint(c,size=(n,))\n",
    "print(target.shape)\n",
    "\n",
    "loss = loss_func(y_out, target)\n",
    "print(loss.item())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([[-1.1258, -1.1524],\n",
      "        [-0.2506, -0.4339],\n",
      "        [ 0.8487,  0.6920],\n",
      "        [-0.3160, -2.1152],\n",
      "        [ 0.3223, -1.2633],\n",
      "        [ 0.3500,  0.3081],\n",
      "        [ 0.1198,  1.2377],\n",
      "        [ 1.1168, -0.2473]])\n"
     ]
    }
   ],
   "source": [
    "loss.backward()\n",
    "print (y.data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Defining Optimizer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "from torch import optim\n",
    "opt = optim.Adam(cnn_model.parameters(), lr=3e-4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "current lr=0.0003\n"
     ]
    }
   ],
   "source": [
    "# get learning rate \n",
    "def get_lr(opt):\n",
    "    for param_group in opt.param_groups:\n",
    "        return param_group['lr']\n",
    "\n",
    "current_lr=get_lr(opt)\n",
    "print('current lr={}'.format(current_lr))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "from torch.optim.lr_scheduler import ReduceLROnPlateau\n",
    "\n",
    "# define learning rate scheduler\n",
    "lr_scheduler = ReduceLROnPlateau(opt, mode='min',factor=0.5, patience=20,verbose=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch    21: reducing learning rate of group 0 to 1.5000e-04.\n",
      "Epoch    42: reducing learning rate of group 0 to 7.5000e-05.\n",
      "Epoch    63: reducing learning rate of group 0 to 3.7500e-05.\n",
      "Epoch    84: reducing learning rate of group 0 to 1.8750e-05.\n"
     ]
    }
   ],
   "source": [
    "for i in range(100):\n",
    "    lr_scheduler.step(1)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Training and Evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "def metrics_batch(output, target):\n",
    "    # get output class\n",
    "    pred = output.argmax(dim=1, keepdim=True)\n",
    "    \n",
    "    # compare output class with target class\n",
    "    corrects=pred.eq(target.view_as(pred)).sum().item()\n",
    "    return corrects"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([[ 0.4681, -0.1577],\n",
      "        [ 1.4437,  0.2660],\n",
      "        [ 0.1665,  0.8744],\n",
      "        [-0.1435, -0.1116],\n",
      "        [ 0.9318,  1.2590],\n",
      "        [ 2.0050,  0.0537],\n",
      "        [ 0.6181, -0.4128],\n",
      "        [-0.8411, -2.3160]], requires_grad=True)\n",
      "torch.Size([8, 2])\n",
      "torch.Size([8])\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "3"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "n,c=8,2\n",
    "output = torch.randn(n, c, requires_grad=True)\n",
    "print (output)\n",
    "print(output.shape)\n",
    "\n",
    "#target = torch.randint(c,size=(n,))\n",
    "target = torch.ones(n,dtype=torch.long)\n",
    "print(target.shape)\n",
    "\n",
    "metrics_batch(output,target)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "def loss_batch(loss_func, output, target, opt=None):\n",
    "    \n",
    "    # get loss \n",
    "    loss = loss_func(output, target)\n",
    "    \n",
    "    # get performance metric\n",
    "    metric_b = metrics_batch(output,target)\n",
    "    \n",
    "    if opt is not None:\n",
    "        opt.zero_grad()\n",
    "        loss.backward()\n",
    "        opt.step()\n",
    "\n",
    "    return loss.item(), metric_b\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "# define device as a global variable\n",
    "device = torch.device(\"cuda\")\n",
    "\n",
    "def loss_epoch(model,loss_func,dataset_dl,sanity_check=False,opt=None):\n",
    "    running_loss=0.0\n",
    "    running_metric=0.0\n",
    "    len_data=len(dataset_dl.dataset)\n",
    "\n",
    "    for xb, yb in dataset_dl:\n",
    "        # move batch to device\n",
    "        xb=xb.to(device)\n",
    "        yb=yb.to(device)\n",
    "        \n",
    "        # get model output\n",
    "        output=model(xb)\n",
    "        \n",
    "        # get loss per batch\n",
    "        loss_b,metric_b=loss_batch(loss_func, output, yb, opt)\n",
    "        \n",
    "        # update running loss\n",
    "        running_loss+=loss_b\n",
    "        \n",
    "        # update running metric\n",
    "        if metric_b is not None:\n",
    "            running_metric+=metric_b\n",
    "\n",
    "        # break the loop in case of sanity check\n",
    "        if sanity_check is True:\n",
    "            break\n",
    "    \n",
    "    # average loss value\n",
    "    loss=running_loss/float(len_data)\n",
    "    \n",
    "    # average metric value\n",
    "    metric=running_metric/float(len_data)\n",
    "    \n",
    "    return loss, metric"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "def train_val(model, params):\n",
    "    # extract model parameters\n",
    "    num_epochs=params[\"num_epochs\"]\n",
    "    loss_func=params[\"loss_func\"]\n",
    "    opt=params[\"optimizer\"]\n",
    "    train_dl=params[\"train_dl\"]\n",
    "    val_dl=params[\"val_dl\"]\n",
    "    sanity_check=params[\"sanity_check\"]\n",
    "    lr_scheduler=params[\"lr_scheduler\"]\n",
    "    path2weights=params[\"path2weights\"]\n",
    "    \n",
    "    # history of loss values in each epoch\n",
    "    loss_history={\n",
    "        \"train\": [],\n",
    "        \"val\": [],\n",
    "    }\n",
    "    \n",
    "    # histroy of metric values in each epoch\n",
    "    metric_history={\n",
    "        \"train\": [],\n",
    "        \"val\": [],\n",
    "    }\n",
    "    \n",
    "    # a deep copy of weights for the best performing model\n",
    "    best_model_wts = copy.deepcopy(model.state_dict())\n",
    "    \n",
    "    # initialize best loss to a large value\n",
    "    best_loss=float('inf')\n",
    "    \n",
    "    # main loop\n",
    "    for epoch in range(num_epochs):\n",
    "        \n",
    "        # get current learning rate\n",
    "        current_lr=get_lr(opt)\n",
    "        print('Epoch {}/{}, current lr={}'.format(epoch, num_epochs - 1, current_lr))\n",
    "        \n",
    "        # train model on training dataset\n",
    "        model.train()\n",
    "        train_loss, train_metric=loss_epoch(model,loss_func,train_dl,sanity_check,opt)\n",
    "\n",
    "        # collect loss and metric for training dataset\n",
    "        loss_history[\"train\"].append(train_loss)\n",
    "        metric_history[\"train\"].append(train_metric)\n",
    "        \n",
    "        # evaluate model on validation dataset    \n",
    "        model.eval()\n",
    "        with torch.no_grad():\n",
    "            val_loss, val_metric=loss_epoch(model,loss_func,val_dl,sanity_check)\n",
    "        \n",
    "       \n",
    "        # store best model\n",
    "        if val_loss < best_loss:\n",
    "            best_loss = val_loss\n",
    "            best_model_wts = copy.deepcopy(model.state_dict())\n",
    "            \n",
    "            # store weights into a local file\n",
    "            torch.save(model.state_dict(), path2weights)\n",
    "            print(\"Copied best model weights!\")\n",
    "        \n",
    "        # collect loss and metric for validation dataset\n",
    "        loss_history[\"val\"].append(val_loss)\n",
    "        metric_history[\"val\"].append(val_metric)\n",
    "        \n",
    "        # learning rate schedule\n",
    "        lr_scheduler.step(val_loss)\n",
    "        if current_lr != get_lr(opt):\n",
    "            print(\"Loading best model weights!\")\n",
    "            model.load_state_dict(best_model_wts) \n",
    "\n",
    "        print(\"train loss: %.6f, dev loss: %.6f, accuracy: %.2f\" %(train_loss,val_loss,100*val_metric))\n",
    "        print(\"-\"*10) \n",
    "\n",
    "    # load best model weights\n",
    "    model.load_state_dict(best_model_wts)\n",
    "        \n",
    "    return model, loss_history, metric_history"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 0/99, current lr=0.0003\n",
      "Copied best model weights!\n",
      "train loss: 0.000129, dev loss: 0.001024, accuracy: 0.05\n",
      "----------\n",
      "Epoch 1/99, current lr=0.0003\n",
      "Copied best model weights!\n",
      "train loss: 0.000125, dev loss: 0.001021, accuracy: 0.05\n",
      "----------\n",
      "Epoch 2/99, current lr=0.0003\n",
      "Copied best model weights!\n",
      "train loss: 0.000127, dev loss: 0.001016, accuracy: 0.05\n",
      "----------\n",
      "Epoch 3/99, current lr=0.0003\n",
      "Copied best model weights!\n",
      "train loss: 0.000127, dev loss: 0.001012, accuracy: 0.06\n",
      "----------\n",
      "Epoch 4/99, current lr=0.0003\n",
      "Copied best model weights!\n",
      "train loss: 0.000127, dev loss: 0.001009, accuracy: 0.07\n",
      "----------\n",
      "Epoch 5/99, current lr=0.0003\n",
      "Copied best model weights!\n",
      "train loss: 0.000126, dev loss: 0.001005, accuracy: 0.09\n",
      "----------\n",
      "Epoch 6/99, current lr=0.0003\n",
      "Copied best model weights!\n",
      "train loss: 0.000126, dev loss: 0.000999, accuracy: 0.09\n",
      "----------\n",
      "Epoch 7/99, current lr=0.0003\n",
      "Copied best model weights!\n",
      "train loss: 0.000125, dev loss: 0.000994, accuracy: 0.09\n",
      "----------\n",
      "Epoch 8/99, current lr=0.0003\n",
      "Copied best model weights!\n",
      "train loss: 0.000126, dev loss: 0.000985, accuracy: 0.09\n",
      "----------\n",
      "Epoch 9/99, current lr=0.0003\n",
      "Copied best model weights!\n",
      "train loss: 0.000122, dev loss: 0.000983, accuracy: 0.09\n",
      "----------\n",
      "Epoch 10/99, current lr=0.0003\n",
      "Copied best model weights!\n",
      "train loss: 0.000125, dev loss: 0.000978, accuracy: 0.09\n",
      "----------\n",
      "Epoch 11/99, current lr=0.0003\n",
      "Copied best model weights!\n",
      "train loss: 0.000124, dev loss: 0.000973, accuracy: 0.09\n",
      "----------\n",
      "Epoch 12/99, current lr=0.0003\n",
      "Copied best model weights!\n",
      "train loss: 0.000120, dev loss: 0.000965, accuracy: 0.09\n",
      "----------\n",
      "Epoch 13/99, current lr=0.0003\n",
      "Copied best model weights!\n",
      "train loss: 0.000121, dev loss: 0.000962, accuracy: 0.09\n",
      "----------\n",
      "Epoch 14/99, current lr=0.0003\n",
      "Copied best model weights!\n",
      "train loss: 0.000118, dev loss: 0.000954, accuracy: 0.09\n",
      "----------\n",
      "Epoch 15/99, current lr=0.0003\n",
      "Copied best model weights!\n",
      "train loss: 0.000122, dev loss: 0.000953, accuracy: 0.09\n",
      "----------\n",
      "Epoch 16/99, current lr=0.0003\n",
      "Copied best model weights!\n",
      "train loss: 0.000121, dev loss: 0.000948, accuracy: 0.09\n",
      "----------\n",
      "Epoch 17/99, current lr=0.0003\n",
      "train loss: 0.000116, dev loss: 0.000958, accuracy: 0.09\n",
      "----------\n",
      "Epoch 18/99, current lr=0.0003\n",
      "Copied best model weights!\n",
      "train loss: 0.000108, dev loss: 0.000930, accuracy: 0.09\n",
      "----------\n",
      "Epoch 19/99, current lr=0.0003\n",
      "train loss: 0.000133, dev loss: 0.000961, accuracy: 0.09\n",
      "----------\n",
      "Epoch 20/99, current lr=0.0003\n",
      "train loss: 0.000138, dev loss: 0.000959, accuracy: 0.09\n",
      "----------\n",
      "Epoch 21/99, current lr=0.0003\n",
      "train loss: 0.000123, dev loss: 0.000962, accuracy: 0.09\n",
      "----------\n",
      "Epoch 22/99, current lr=0.0003\n",
      "train loss: 0.000139, dev loss: 0.000957, accuracy: 0.09\n",
      "----------\n",
      "Epoch 23/99, current lr=0.0003\n",
      "train loss: 0.000126, dev loss: 0.000941, accuracy: 0.09\n",
      "----------\n",
      "Epoch 24/99, current lr=0.0003\n",
      "train loss: 0.000119, dev loss: 0.000947, accuracy: 0.09\n",
      "----------\n",
      "Epoch 25/99, current lr=0.0003\n",
      "train loss: 0.000125, dev loss: 0.000955, accuracy: 0.09\n",
      "----------\n",
      "Epoch 26/99, current lr=0.0003\n",
      "train loss: 0.000120, dev loss: 0.000947, accuracy: 0.09\n",
      "----------\n",
      "Epoch 27/99, current lr=0.0003\n",
      "train loss: 0.000120, dev loss: 0.000954, accuracy: 0.09\n",
      "----------\n",
      "Epoch 28/99, current lr=0.0003\n",
      "train loss: 0.000124, dev loss: 0.000954, accuracy: 0.09\n",
      "----------\n",
      "Epoch 29/99, current lr=0.0003\n",
      "train loss: 0.000122, dev loss: 0.000965, accuracy: 0.09\n",
      "----------\n",
      "Epoch 30/99, current lr=0.0003\n",
      "train loss: 0.000122, dev loss: 0.000952, accuracy: 0.09\n",
      "----------\n",
      "Epoch 31/99, current lr=0.0003\n",
      "train loss: 0.000121, dev loss: 0.000956, accuracy: 0.09\n",
      "----------\n",
      "Epoch 32/99, current lr=0.0003\n",
      "train loss: 0.000116, dev loss: 0.000955, accuracy: 0.09\n",
      "----------\n",
      "Epoch 33/99, current lr=0.0003\n",
      "train loss: 0.000119, dev loss: 0.000955, accuracy: 0.09\n",
      "----------\n",
      "Epoch 34/99, current lr=0.0003\n",
      "train loss: 0.000116, dev loss: 0.000964, accuracy: 0.09\n",
      "----------\n",
      "Epoch 35/99, current lr=0.0003\n",
      "train loss: 0.000126, dev loss: 0.000967, accuracy: 0.09\n",
      "----------\n",
      "Epoch 36/99, current lr=0.0003\n",
      "train loss: 0.000117, dev loss: 0.000959, accuracy: 0.09\n",
      "----------\n",
      "Epoch 37/99, current lr=0.0003\n",
      "train loss: 0.000123, dev loss: 0.000956, accuracy: 0.09\n",
      "----------\n",
      "Epoch 38/99, current lr=0.0003\n",
      "train loss: 0.000125, dev loss: 0.000955, accuracy: 0.09\n",
      "----------\n",
      "Epoch 39/99, current lr=0.0003\n",
      "Epoch    39: reducing learning rate of group 0 to 1.5000e-04.\n",
      "Loading best model weights!\n",
      "train loss: 0.000122, dev loss: 0.000957, accuracy: 0.09\n",
      "----------\n",
      "Epoch 40/99, current lr=0.00015\n",
      "train loss: 0.000147, dev loss: 0.000948, accuracy: 0.09\n",
      "----------\n",
      "Epoch 41/99, current lr=0.00015\n",
      "train loss: 0.000126, dev loss: 0.000943, accuracy: 0.09\n",
      "----------\n",
      "Epoch 42/99, current lr=0.00015\n",
      "train loss: 0.000125, dev loss: 0.000942, accuracy: 0.09\n",
      "----------\n",
      "Epoch 43/99, current lr=0.00015\n",
      "train loss: 0.000126, dev loss: 0.000953, accuracy: 0.09\n",
      "----------\n",
      "Epoch 44/99, current lr=0.00015\n",
      "train loss: 0.000123, dev loss: 0.000947, accuracy: 0.09\n",
      "----------\n",
      "Epoch 45/99, current lr=0.00015\n",
      "train loss: 0.000126, dev loss: 0.000959, accuracy: 0.09\n",
      "----------\n",
      "Epoch 46/99, current lr=0.00015\n",
      "train loss: 0.000142, dev loss: 0.000952, accuracy: 0.09\n",
      "----------\n",
      "Epoch 47/99, current lr=0.00015\n",
      "train loss: 0.000117, dev loss: 0.000947, accuracy: 0.09\n",
      "----------\n",
      "Epoch 48/99, current lr=0.00015\n",
      "train loss: 0.000116, dev loss: 0.000951, accuracy: 0.09\n",
      "----------\n",
      "Epoch 49/99, current lr=0.00015\n",
      "train loss: 0.000128, dev loss: 0.000955, accuracy: 0.09\n",
      "----------\n",
      "Epoch 50/99, current lr=0.00015\n",
      "train loss: 0.000123, dev loss: 0.000954, accuracy: 0.09\n",
      "----------\n",
      "Epoch 51/99, current lr=0.00015\n",
      "train loss: 0.000125, dev loss: 0.000959, accuracy: 0.09\n",
      "----------\n",
      "Epoch 52/99, current lr=0.00015\n",
      "train loss: 0.000123, dev loss: 0.000955, accuracy: 0.09\n",
      "----------\n",
      "Epoch 53/99, current lr=0.00015\n",
      "train loss: 0.000129, dev loss: 0.000953, accuracy: 0.09\n",
      "----------\n",
      "Epoch 54/99, current lr=0.00015\n",
      "train loss: 0.000128, dev loss: 0.000959, accuracy: 0.09\n",
      "----------\n",
      "Epoch 55/99, current lr=0.00015\n",
      "train loss: 0.000124, dev loss: 0.000956, accuracy: 0.09\n",
      "----------\n",
      "Epoch 56/99, current lr=0.00015\n",
      "train loss: 0.000123, dev loss: 0.000957, accuracy: 0.09\n",
      "----------\n",
      "Epoch 57/99, current lr=0.00015\n",
      "train loss: 0.000122, dev loss: 0.000964, accuracy: 0.09\n",
      "----------\n",
      "Epoch 58/99, current lr=0.00015\n",
      "train loss: 0.000126, dev loss: 0.000960, accuracy: 0.09\n",
      "----------\n",
      "Epoch 59/99, current lr=0.00015\n",
      "train loss: 0.000125, dev loss: 0.000966, accuracy: 0.09\n",
      "----------\n",
      "Epoch 60/99, current lr=0.00015\n",
      "Epoch    60: reducing learning rate of group 0 to 7.5000e-05.\n",
      "Loading best model weights!\n",
      "train loss: 0.000117, dev loss: 0.000958, accuracy: 0.09\n",
      "----------\n",
      "Epoch 61/99, current lr=7.5e-05\n",
      "train loss: 0.000115, dev loss: 0.000962, accuracy: 0.09\n",
      "----------\n",
      "Epoch 62/99, current lr=7.5e-05\n",
      "train loss: 0.000124, dev loss: 0.000962, accuracy: 0.09\n",
      "----------\n",
      "Epoch 63/99, current lr=7.5e-05\n",
      "train loss: 0.000109, dev loss: 0.000959, accuracy: 0.09\n",
      "----------\n",
      "Epoch 64/99, current lr=7.5e-05\n",
      "train loss: 0.000121, dev loss: 0.000957, accuracy: 0.09\n",
      "----------\n",
      "Epoch 65/99, current lr=7.5e-05\n",
      "train loss: 0.000137, dev loss: 0.000969, accuracy: 0.09\n",
      "----------\n",
      "Epoch 66/99, current lr=7.5e-05\n",
      "train loss: 0.000119, dev loss: 0.000955, accuracy: 0.09\n",
      "----------\n",
      "Epoch 67/99, current lr=7.5e-05\n",
      "train loss: 0.000125, dev loss: 0.000934, accuracy: 0.09\n",
      "----------\n",
      "Epoch 68/99, current lr=7.5e-05\n",
      "train loss: 0.000118, dev loss: 0.000947, accuracy: 0.09\n",
      "----------\n",
      "Epoch 69/99, current lr=7.5e-05\n",
      "train loss: 0.000128, dev loss: 0.000945, accuracy: 0.09\n",
      "----------\n",
      "Epoch 70/99, current lr=7.5e-05\n",
      "train loss: 0.000117, dev loss: 0.000969, accuracy: 0.09\n",
      "----------\n",
      "Epoch 71/99, current lr=7.5e-05\n",
      "train loss: 0.000135, dev loss: 0.000954, accuracy: 0.09\n",
      "----------\n",
      "Epoch 72/99, current lr=7.5e-05\n",
      "train loss: 0.000134, dev loss: 0.000954, accuracy: 0.09\n",
      "----------\n",
      "Epoch 73/99, current lr=7.5e-05\n",
      "train loss: 0.000132, dev loss: 0.000962, accuracy: 0.09\n",
      "----------\n",
      "Epoch 74/99, current lr=7.5e-05\n",
      "train loss: 0.000124, dev loss: 0.000951, accuracy: 0.09\n",
      "----------\n",
      "Epoch 75/99, current lr=7.5e-05\n",
      "train loss: 0.000116, dev loss: 0.000957, accuracy: 0.09\n",
      "----------\n",
      "Epoch 76/99, current lr=7.5e-05\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "train loss: 0.000129, dev loss: 0.000955, accuracy: 0.09\n",
      "----------\n",
      "Epoch 77/99, current lr=7.5e-05\n",
      "train loss: 0.000118, dev loss: 0.000946, accuracy: 0.09\n",
      "----------\n",
      "Epoch 78/99, current lr=7.5e-05\n",
      "train loss: 0.000134, dev loss: 0.000945, accuracy: 0.09\n",
      "----------\n",
      "Epoch 79/99, current lr=7.5e-05\n",
      "train loss: 0.000123, dev loss: 0.000958, accuracy: 0.09\n",
      "----------\n",
      "Epoch 80/99, current lr=7.5e-05\n",
      "train loss: 0.000133, dev loss: 0.000943, accuracy: 0.09\n",
      "----------\n",
      "Epoch 81/99, current lr=7.5e-05\n",
      "Epoch    81: reducing learning rate of group 0 to 3.7500e-05.\n",
      "Loading best model weights!\n",
      "train loss: 0.000119, dev loss: 0.000946, accuracy: 0.09\n",
      "----------\n",
      "Epoch 82/99, current lr=3.75e-05\n",
      "train loss: 0.000106, dev loss: 0.000960, accuracy: 0.09\n",
      "----------\n",
      "Epoch 83/99, current lr=3.75e-05\n",
      "train loss: 0.000112, dev loss: 0.000947, accuracy: 0.09\n",
      "----------\n",
      "Epoch 84/99, current lr=3.75e-05\n",
      "train loss: 0.000110, dev loss: 0.000958, accuracy: 0.09\n",
      "----------\n",
      "Epoch 85/99, current lr=3.75e-05\n",
      "train loss: 0.000117, dev loss: 0.000943, accuracy: 0.09\n",
      "----------\n",
      "Epoch 86/99, current lr=3.75e-05\n",
      "train loss: 0.000116, dev loss: 0.000952, accuracy: 0.09\n",
      "----------\n",
      "Epoch 87/99, current lr=3.75e-05\n",
      "train loss: 0.000123, dev loss: 0.000956, accuracy: 0.09\n",
      "----------\n",
      "Epoch 88/99, current lr=3.75e-05\n",
      "train loss: 0.000132, dev loss: 0.000955, accuracy: 0.09\n",
      "----------\n",
      "Epoch 89/99, current lr=3.75e-05\n",
      "train loss: 0.000107, dev loss: 0.000953, accuracy: 0.09\n",
      "----------\n",
      "Epoch 90/99, current lr=3.75e-05\n",
      "train loss: 0.000129, dev loss: 0.000974, accuracy: 0.09\n",
      "----------\n",
      "Epoch 91/99, current lr=3.75e-05\n",
      "train loss: 0.000115, dev loss: 0.000955, accuracy: 0.09\n",
      "----------\n",
      "Epoch 92/99, current lr=3.75e-05\n",
      "train loss: 0.000104, dev loss: 0.000966, accuracy: 0.09\n",
      "----------\n",
      "Epoch 93/99, current lr=3.75e-05\n",
      "train loss: 0.000130, dev loss: 0.000959, accuracy: 0.09\n",
      "----------\n",
      "Epoch 94/99, current lr=3.75e-05\n",
      "train loss: 0.000125, dev loss: 0.000941, accuracy: 0.09\n",
      "----------\n",
      "Epoch 95/99, current lr=3.75e-05\n",
      "train loss: 0.000134, dev loss: 0.000949, accuracy: 0.09\n",
      "----------\n",
      "Epoch 96/99, current lr=3.75e-05\n",
      "train loss: 0.000118, dev loss: 0.000958, accuracy: 0.09\n",
      "----------\n",
      "Epoch 97/99, current lr=3.75e-05\n",
      "train loss: 0.000115, dev loss: 0.000953, accuracy: 0.09\n",
      "----------\n",
      "Epoch 98/99, current lr=3.75e-05\n",
      "train loss: 0.000118, dev loss: 0.000955, accuracy: 0.09\n",
      "----------\n",
      "Epoch 99/99, current lr=3.75e-05\n",
      "train loss: 0.000140, dev loss: 0.000960, accuracy: 0.09\n",
      "----------\n"
     ]
    }
   ],
   "source": [
    "import copy\n",
    "\n",
    "loss_func = nn.NLLLoss(reduction=\"sum\")\n",
    "opt = optim.Adam(cnn_model.parameters(), lr=3e-4)\n",
    "lr_scheduler = ReduceLROnPlateau(opt, mode='min',factor=0.5, patience=20,verbose=1)\n",
    "\n",
    "params_train={\n",
    " \"num_epochs\": 100,\n",
    " \"optimizer\": opt,\n",
    " \"loss_func\": loss_func,\n",
    " \"train_dl\": train_dl,\n",
    " \"val_dl\": val_dl,\n",
    " \"sanity_check\": True,\n",
    " \"lr_scheduler\": lr_scheduler,\n",
    " \"path2weights\": \"./models/weights.pt\",\n",
    "}\n",
    "\n",
    "# train and validate the model\n",
    "cnn_model,loss_hist,metric_hist=train_val(cnn_model,params_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Train-Validation Progress\n",
    "num_epochs=params_train[\"num_epochs\"]\n",
    "\n",
    "# plot loss progress\n",
    "plt.title(\"Train-Val Loss\")\n",
    "plt.plot(range(1,num_epochs+1),loss_hist[\"train\"],label=\"train\")\n",
    "plt.plot(range(1,num_epochs+1),loss_hist[\"val\"],label=\"val\")\n",
    "plt.ylabel(\"Loss\")\n",
    "plt.xlabel(\"Training Epochs\")\n",
    "plt.legend()\n",
    "plt.show()\n",
    "\n",
    "# plot accuracy progress\n",
    "plt.title(\"Train-Val Accuracy\")\n",
    "plt.plot(range(1,num_epochs+1),metric_hist[\"train\"],label=\"train\")\n",
    "plt.plot(range(1,num_epochs+1),metric_hist[\"val\"],label=\"val\")\n",
    "plt.ylabel(\"Accuracy\")\n",
    "plt.xlabel(\"Training Epochs\")\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 0/1, current lr=0.0003\n",
      "Copied best model weights!\n",
      "train loss: 0.454370, dev loss: 0.409485, accuracy: 81.75\n",
      "----------\n",
      "Epoch 1/1, current lr=0.0003\n",
      "Copied best model weights!\n",
      "train loss: 0.381786, dev loss: 0.395184, accuracy: 81.68\n",
      "----------\n"
     ]
    }
   ],
   "source": [
    "import copy\n",
    "\n",
    "loss_func = nn.NLLLoss(reduction=\"sum\")\n",
    "opt = optim.Adam(cnn_model.parameters(), lr=3e-4)\n",
    "lr_scheduler = ReduceLROnPlateau(opt, mode='min',factor=0.5, patience=20,verbose=1)\n",
    "\n",
    "params_train={\n",
    " \"num_epochs\": 2,\n",
    " \"optimizer\": opt,\n",
    " \"loss_func\": loss_func,\n",
    " \"train_dl\": train_dl,\n",
    " \"val_dl\": val_dl,\n",
    " \"sanity_check\": False,\n",
    " \"lr_scheduler\": lr_scheduler,\n",
    " \"path2weights\": \"./models/weights.pt\",\n",
    "}\n",
    "\n",
    "# train and validate the model\n",
    "cnn_model,loss_hist,metric_hist=train_val(cnn_model,params_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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iDHNmp3PZhGGEh+oVRxERkZNBYU1OWFOT458f7WDemz4+2r6f5P59uH12OldMTCYyLNTr8kRERLo1hTU5aZxz5H68i3m5Pt7bupchfaO4dWYaV04eQVS4QpuIiMjxUFiTk845x3Lfbua/6WPNpgoSYyO5ZWYq15w9kphIra0sIiJyLBTWJKBWF+9hQa6P5b7d9I8O56YZaVw3dSTxUeFHvlhEREQU1qRrrNv8GQvzfOR+vIv4qDCun5bKjdNS6Bcd4XVpIiIiQU1hTbrUh9v2MT+3kNc37CQmIpRvn5PCd6enkhgb6XVpIiIiQUlhTTzx8Y79LMwr4pX3y4gMC+Gas0dy68w0kuJPrM2GiIhIT6OwJp4qKq9kYZ6Pl9aXERpifGvScG6bnc6wfn28Lk1ERCQoKKxJUNiyp5oHlvp4fl0pAF8/M5k5szMYkRDtcWUiIiLeUliToLJt70EeWlrEs2u30tjkuPSMoczJyiAjKdbr0kRERDyhsCZBadf+GhYvK+apt7dQ09DIReOGMDc7g9MGx3tdmoiISJdSWJOgtqeylkeWl/DEqs1U1jZw/uhBzM3OZFxyX69LExER6RIKa9It7K2u47EVm3hsRQn7axqYfepA5mZnMnFkf69LExERCSiFNelWDtTU88SqzTyyvISKqjqmZSSQk5XJlLQBmJnX5YmIiJx0CmvSLVXXNfDU6i08tKyY3ZW1nJXSn7nZmczITFRoExGRHuVYwlpIgAu5wMw+MTOfmd3TyXlXmJkzs0lt9o8ws0oz+2Eg65TgEB0Rxs0z01h+dxa/uGQMpZ8d5NuPruGyRSt546Od9JR/sRARETkWAQtrZhYKLAQuBEYDV5nZ6HbOiwPuBN5u5zb3A68FqkYJTlHhoXznnBTyfzSb//raOCqqarnpiQIumrecVz/YTlOTQpuIiPQegRxZmwz4nHPFzrk64Fng0nbO+yVwH1DTeqeZXQYUAxsCWKMEsciwUK6aPILcu2bzu2+cQW19I3Oeeocv/2EZL63fRqNCm4iI9AKBDGvDgK2ttkv9+w4xswnAcOfcK232xwB3A78IYH3STYSHhnDFxGT+9YNZzLtqAmbwvWfX86XfL2VJwVbqG5u8LlFERCRgAhnW2nsj/NBQiJmF0PyY8652zvsFcL9zrrLTLzC7xcwKzKygvLz8hIqV4BcaYlxyxlD+8b2ZPHjtmURHhPLj598n63f5PPX2ZmobGr0uUURE5KQL2GxQM5sK/Nw592X/9k8AnHP/5d/uCxQBLYFsMFABXEJziBvu398PaAL+r3NuQUffp9mgvY9zjrxPdjHvTR/rt+5lcHwUt85K46rJI4gKD/W6PBERkQ4FxdIdZhYGfAqcC2wD1gJXO+fafQfNzPKBHzrnCtrs/zlQ6Zz7XWffp7DWeznnWOHbw7zcQtaUVJAYG8ktM1O55uyRxESGeV2eiIjIYYJi6Q7nXAOQA7wObASWOOc2mNm9ZnZJoL5Xeh8zY3pmIktuncpzt0xh1JA4/t+rHzP9N7ksyC1kf0291yWKiIgcNy2KKz3SO1s+Y0Guj9yPdxEXFcYN56Rw4/RU+kVHeF2aiIhIcDwG7WoKa9KeD7ftY0Guj39s2EFMRCjXTU3hphmpJMZGel2aiIj0YgprIm18suMAC/J8vPJ+GZFhIVw9eSS3zkpjUHyU16WJiEgvpLAm0oGi8koW5RXxv+u3EWrGN89K5rZZ6ST3j/a6NBER6UUU1kSOYMueah5Y6uP5daU4B18/M5k5WemMTIjxujQREekFFNZEjlLZ3oM8tLSIZ9ZupaGxiUvHD+OOrAwykmK9Lk1ERHowhTWRY7Rrfw1/fKuYP6/eQk1DIxeNG0JOVgajhsR7XZqIiPRACmsix2lPZS2PLC/hiVWbqaxt4LzRg7gzO5NxyX29Lk1ERHoQhTWRE7Svup7HVpbw6PIS9tc0MPvUgczNzmDiyAFelyYiIj2AwprISXKgpp4nV2/m4bdKqKiq45z0BOZmZzIlbQBm5nV5IiLSTSmsiZxk1XUNPP32Fh5aVkz5gVrOSulPTnYmMzMTFdpEROSYKayJBEhNfSNLCrbyYH4RZftqOCO5LznZmXxpVJJCm4iIHDWFNZEAq2to4oV3SlmU72NrxUFGDYlnbnYGF4wZTEiIQpuIiHROYU2kizQ0NvHS+jIW5vko3l1FRlIsOVkZXHz6EMJCQ7wuT0REgpTCmkgXa2xyvPrBdhbk+vhk5wFSEqKZk5XB5ROGEa7QJiIibSisiXikqcnxz492siCvkA+37WdYvz7cPjudb0xKJjIs1OvyREQkSCisiXjMOUf+J+XMyy3k3S17GRQfya0z07lq8gj6RCi0iYj0dgprIkHCOcfKoj3Me7OQt0sqSIyN4OYZaVw7ZSQxkWFelyciIh5RWBMJQmtKKpifW8hbhbvpFx3Od6el8p1pKcRHhXtdmoiIdDGFNZEg9u6Wz1iQ6+PNj3cRFxXGDeekcMO0VPrHRHhdmoiIdBGFNZFu4MNt+1iQ6+MfG3YQExHKtVNHcvOMNBJjI70uTUREAkxhTaQb+XTnARbk+njl/TIiwkK4avIIbp2ZzuC+UV6XJiIiAaKwJtINFZdXsii/iBff3UaoGd88K5nbZqWT3D/a69JEROQkU1gT6ca2VlSzKL+I59dtxTn42pnDmDM7g5TEGK9LExGRk0RhTaQH2L7vIA8tLeaZNVuob2zi0vHDuCMrnYykOK9LExGRE6SwJtKD7DpQw8NvlfDkqs3UNDRy0dgh5GRnMGpIvNeliYjIcVJYE+mBKqrqeGR5MY+v3ExlbQPnjR7E3OwMTk/u53VpIiJyjI4lrAW0w7SZXWBmn5iZz8zu6eS8K8zMmdkk//ZkM1vv/7xnZpcHsk6R7mBATAQ/+vJprLg7m+9/6RTWlFRwyYIVfOfRNazbXOF1eSIiEiABG1kzs1DgU+A8oBRYC1zlnPuozXlxwN+BCCDHOVdgZtFAnXOuwcyGAO8BQ51zDR19n0bWpLc5UFPPk6s38/BbJVRU1TE1LYG552YwNS0BM/O6PBER6USwjKxNBnzOuWLnXB3wLHBpO+f9ErgPqGnZ4ZyrbhXMooCe8axW5CSKiwpnzuwMlt+dxX98ZRRF5ZVc/ce3+caDq8j/ZBc95RUHEZHeLpBhbRiwtdV2qX/fIWY2ARjunHul7cVmdraZbQA+AG5rb1TNzG4xswIzKygvLz+51Yt0E9ERYdw0I41lP87il5eOoWzvQa5/bC2XLlzBvz7aqdAmItLNBTKstfcc5tD/a5hZCHA/cFd7Fzvn3nbOjQHOAn5iZoct5+6cW+ycm+ScmzRw4MCTVLZI9xQVHsp1U1PI/1EWv/7aOPZW13PzEwVc+D9v8ff3t9PUpNAmItIdBTKslQLDW20nA2WttuOAsUC+mW0CpgAvt0wyaOGc2whU+c8VkSOICAvhyskjyL1rFr//5hnUNTZxx9PvcP4flvHiu6U0NDZ5XaKIiByDQIa1tUCmmaWaWQRwJfByy0Hn3D7nXKJzLsU5lwKsBi7xTzBINbMwADMbCZwKbApgrSI9TlhoCF87M5l/fX8WC66eQFiI8f3n3uNLv1/KkrVbqWtQaBMR6Q4CFtb875jlAK8DG4ElzrkNZnavmV1yhMunA++Z2XrgRWCOc253oGoV6clCQ4yLTx/Kq3fO4KHrJhIbFcaPX3ifrN/l8+TqzdQ2NHpdooiIdEKL4or0Ms458j8tZ/6bhbyzZS+D4iO5dWY6V00eQZ+IUK/LExHpFdTBQESOyDnHyqI9zHuzkLdLKkiMjeCmGWlcO2UksZFhXpcnItKjKayJyDFZU1LB/NxC3ircTb/ocG6clsp3zkmhb59wr0sTEemRFNZE5Lis37qXBbmFvLFxF3GRYVw/LYUbp6XSPybC69JERHoUhTUROSEbyvaxINfHax/uIDoilOumjOSmGWkMjIv0ujQRkR5BYU1ETopPdx5gYZ6Pv71XRkRYCFdNHsGtM9MZ3PewNapFROQYKKyJyElVXF7JA/lFvPjuNkLM+MakZG6fnU5y/2ivSxMR6ZYU1kQkILZWVPPA0iKeLyilyTkunzCMO7IySEmM8bo0EZFuRWFNRAJq+76DPLS0mGfWbKG+sYlLzhhKTnYGGUlxXpcmItItKKyJSJfYdaCGh98q4c+rN3OwvpELxw4mJyuT0UPjvS5NRCSoKayJSJeqqKrj0eUlPL5yEwdqG/jSqEHMzc7gjOH9vC5NRCQoKayJiCf2Hazn8ZWbeGR5CfsO1jPzlIHcmZ3BpJQBXpcmIhJUFNZExFOVtQ08uWozD79VzJ6qOqakDeDO7EympidgZl6XJyLiOYU1EQkK1XUNPLNmKw8tLWLXgVomjuxPTnYGs08ZqNAmIr2awpqIBJWa+kb+UrCVB/KLKNtXw+nJfcnJyuBLowYREqLQJiK9j8KaiASluoYmXny3lIV5RWypqOa0wXHkZGdw4dghhCq0iUgvorAmIkGtobGJv71fxoJcH0XlVaQPjCEnO4Ovnj6UsNAQr8sTEQk4hTUR6RYamxyvfbidBbk+Pt5xgJEJ0cyZnc7lE5KJCFNoE5GeS2FNRLqVpibHGxt3Mj/Xxwfb9jGsXx9um53ONyYmExUe6nV5IiInncKaiHRLzjnyPy1n/puFvLNlL4PiI7llZjpXTx5BnwiFNhHpORTWRKRbc86xqmgP83ILWV1cQUJMBDfNSOO6qSOJjQzzujwRkROmsCYiPcbaTRXMz/Wx7NNy+kWHc+O0VL5zTgp9+4R7XZqIyHFTWBORHmf91r0syPXxxsadxEWG8Z1zUrhxeioDYiK8Lk1E5JgprIlIj7WhbB8L83y89uEO+oSHcu2Ukdw0I5WkuCivSxMROWoKayLS4xXuPMDCPB8vv1dGeGgIV00ewW2z0hncV6FNRIKfwpqI9Bolu6t4IN/HX9/ZRogZV0xK5vZZ6QwfEO11aSIiHTqWsBbQVSfN7AIz+8TMfGZ2TyfnXWFmzswm+bfPM7N1ZvaB/5/ZgaxTRLqv1MQY7rviDPJ+OJtvTErm+YJSsn6Xz4/+8h4lu6u8Lk9E5IQd1ciamaUDpc65WjObDZwOPOGc29vJNaHAp8B5QCmwFrjKOfdRm/PigL8DEUCOc67AzCYAO51zZWY2FnjdOTessxo1siYiANv3HWTxsmKefnsL9Y1NfPWMoeRkZZA5KM7r0kREDgnEyNoLQKOZZQCPAKnA00e4ZjLgc84VO+fqgGeBS9s575fAfUBNyw7n3LvOuTL/5gYgyswij7JWEenFhvTtw8++Oobld2dz84w0/vXRTs7/wzJu//M6NpTt87o8EZFjdrRhrck51wBcDvzBOfd9YMgRrhkGbG21Xerfd4h/BG24c+6VTu7zdeBd51xt2wNmdouZFZhZQXl5+dH8DhHpJQbGRfKTi0ax/O5scrIyWF64m6/MW85Nj69l/dYOHwqIiASdow1r9WZ2FfAdoCVYHWlFSmtn36FnrmYWAtwP3NXhDczGAL8Bbm3vuHNusXNuknNu0sCBA49Qjoj0RgNiIrjr/FNZfk82PzjvFAo2f8ZlC1dw3SNvs3ZThdfliYgc0dGGtRuAqcCvnHMlZpYK/PkI15QCw1ttJwNlrbbjgLFAvpltAqYAL7eaZJAMvAh82zlXdJR1ioi0q2+fcO48N5Pld2dzz4WnsXH7fr7x4Cq+9dAqVvh201NmxotIz3PMS3eYWX+aH12+f4TzwmieYHAusI3mCQZXO+c2dHB+PvBD/wSDfsBS4F7n3AtHU5cmGIjIsThY18gza7bw0LIidu6v5cwR/Zh7biazTxmIWXsPBkRETp6TPsHAzPLNLN7MBgDvAY+Z2e87u8b/jlsO8DqwEVjinNtgZvea2SVH+MocIAP4qZmt93+SjqZWEZGj0ScilBunp7L0R1n88rKx7Nxfyw2PreWSBSt4fcMOmpo00iYiweFol+541zk3wcxuonlU7Wdm9r5z7vTAl3h0NLImIieirqGJ/313GwvzfWzeU81pg+PIyc7gwrFDCA3RSJuInFyBWLojzMyGAN/k8wkGIiI9RkRYCN88azhv/mAW93/rDOobm8h5+l3Ov3/FjfU2AAAgAElEQVQpf32nlIbGJq9LFJFe6mjD2r00P84scs6tNbM0oDBwZYmIeCMsNITLJyTzz+/PYuHVZxIeGsIPlrxH9n8v5dk1W6hrUGgTka6l3qAiIp1oanK8+fEu5ucW8n7pPob168Nts9L4xqThRIWHel2eiHRTgZhgkGxmL5rZLjPbaWYv+JfWEBHp0UJCjPNGD+KlO6bxpxvOYnDfKH760gZm3pfHw28Vc7Cu0esSRaSHO9rHoI8BLwNDae5C8Df/PhGRXsHMmH1qEs/fNpWnbz6b9IGx/OffNzL9N7ksyvdRWdvgdYki0kMd7WzQ9c658Ufa5yU9BhWRrlawqYL5uT6WflpO3z7h3DgtleunpdC3z5EavIhIbxeI2aC7zexaMwv1f64F9hx/iSIi3d+klAE8fuNkXrpjGpNTB3D/G58y/de5/Pb1j6moqvO6PBHpIY52ZG0EsIDmllMOWAnc6ZzbEtjyjp5G1kTEax+V7Wdhno9XP9xOn/BQrp0ykptmpJIUF+V1aSISZI5lZO24Z4Oa2b855/5wXBcHgMKaiASLwp0HWJRfxEvrtxEeGsJVk0dw66w0hvTt43VpIhIkuiqsbXHOjTiuiwNAYU1Egs2m3VUsyvfx13e2YQZXTBzOnNnpDB8Q7XVpIuKxrgprW51zw4/r4gBQWBORYLW1opqHlhWxZG0pjc5x+YRhzJmdTtrAWK9LExGPaGRNRCQI7dhXw0PLinj67S3UNzZx8elDycnO4JRBcV6XJiJd7KSFNTM7QPOEgsMOAX2cc2HHV+LJp7AmIt1F+YFaHl5ezJOrNlNd18gFYwaTk53B2GF9vS5NRLpIl4ysBRuFNRHpbj6rquPRFSX8acUmDtQ2cO5pScw9N5Pxw/t5XZqIBJjCmohIN7LvYD1PrNzEIytK2Ftdz4zMROZmZzI5dYDXpYlIgCisiYh0Q1W1Dfx59Wb++FYxuyvrODt1AHeem8k56QmYmdflichJpLAmItKNHaxr5Jk1W3hoWRE799cyYUQ/7szOZPapAxXaRHoIhTURkR6gtqGRvxSU8kB+Edv2HmTssHhysjI5f/QgQkIU2kS6M4U1EZEepL6xiRff3caiPB+b9lRz6qA4crIzuGjcEEIV2kS6JYU1EZEeqKGxiVfe386CPB++XZWkDYzhjtkZXDp+KGGhIV6XJyLHQGFNRKQHa2py/GPDDubn+ti4fT8jBkRz++x0vn5mMhFhCm0i3YHCmohIL+Cc442Nu5ifW8j7pfsY2jeK22an881Jw4kKD/W6PBHphMKaiEgv4pxjWeFu5r9ZSMHmzxgYF8mtM9O4+uwRREcETaMZEWlFYU1EpBdyzrGqeA8Lcn2sLNrDgJgIbpqRynVTRhIXFe51eSLSisKaiEgvt25zBfNzfeR/Uk7fPuHcMC2FG85JpW+0QptIMDiWsBbQN1HN7AIz+8TMfGZ2TyfnXWFmzswm+bcTzCzPzCrNbEEgaxQR6YkmjhzAn26YzMs505icOoA/vFHItN/kct8/Pqaiqs7r8kTkGARsZM3MQoFPgfOAUmAtcJVz7qM258UBfwcigBznXIGZxQATgLHAWOdczpG+TyNrIiId27h9PwvyfLz6wXaiwkK5dsoIbp6ZRlJclNelifRKwTKyNhnwOeeKnXN1wLPApe2c90vgPqCmZYdzrso5t7z1PhEROX6jhsSz8Ooz+df3Z3LB2ME8sryE6b/J42cvfUjZ3oNelycinQhkWBsGbG21Xerfd4iZTQCGO+deOZ4vMLNbzKzAzArKy8uPv1IRkV4iIymO+781nty7ZnP5+GE89fYWZv02j5/89X22VlR7XZ6ItCOQc7rb64Fy6JmrmYUA9wPXH+8XOOcWA4uh+THo8d7nqDQ2wN++B7FJEDsI4gY1/zN2EMQNhoiYgH69iMjJlJIYw2+uOJ2552bw4NIilqwtZUlBKZeNH8YdWemkDYz1ukQR8QtkWCsFhrfaTgbKWm3H0fxOWr6ZAQwGXjazS5xzwffyWc0+KM6Dyp3Q1HD48YjYVuGtTZCLTYLYwc3b0QkQohXGRSQ4JPeP5j8vG8fc7EweWlrM02s28+K7pXzl9KHkZGVw6uA4r0sU6fUCOcEgjOYJBucC22ieYHC1c25DB+fnAz9sHdTM7HpgUlBNMGhqgoOfQeUOOLADKnc1/12564vbB3ZC3YHDr7fQVqNzrYNckn+7VdAL14u/ItK1dlfW8vBbJTy5ahNVdY1cMGYwOdkZjB3W1+vSRHqUoFlnzcwuAv4AhAKPOud+ZWb3AgXOuZfbnJtPq7BmZpuAeJpnie4Fzm87k7S1oJwNWlfVPBLXXpCr3Pl5yKsqB9d0+PVRfdsPcm1DXp/+YO09dRYROT6fVdXx2IoSHlu5iQM1DWSflsTc7AwmjOjvdWkiPULQhLWuFJRh7Wg1NULV7vaDXNuQ19DOrK3QiI6DXNvtUC2IKSJHb39NPU+s3MQjy0v4rLqeGZmJ5GRlcHZagteliXRrCms9lXNQe8Af5nZ28BjWf6x6T/v3iE7oOMjFDf7878g4jdaJyCFVtQ089fZmFi8rYXdlLZNTB3BndibTMhIw/W+FyDFTWBNoqGt+vNpekDs0euf/NLazmnl4dMfv07UOeTGJEBLa9b9PRDxRU9/IM2u28NDSYnbsr2HCiH7Mzc4g69QkhTaRY6CwJkfPOf+EiQ6CXOuQV7Pv8OstBGIGtjMTtp2QFxHd9b9PRAKitqGR59eVsiiviG17DzJmaDxzszM4f/RgQkIU2kSORGFNAqP+oP+xaztB7gshbxe4xsOvj4zvZLSuVcjr01/Lm4h0E/WNTbz47jYW5fnYtKeaUwfFcUd2Bl8ZN4RQhTaRDimsibeamprfmavc0cFoXasJFHWVh18fEv758iZt16079Bh2UPM5YZFd//tE5DANjU38/YPtLMj1UbirkrTEGOZkZXDp+KGEh+pfvkTaUliT7qO2sp0g184EiqrdtGqA8bk+/dsEuZZHsG1CXlRfTZgQ6QJNTY5/bNjB/FwfG7fvZ/iAPsyZncHXz0wmIkyhTaSFwpr0PI0N/gkT7c2EbRPyGmoOvz4sqs1o3eB2Qt4giEmC0EA29hDpHZxzvLlxF/NzC3mvdB9D+kZx26x0vnXWcKLCNSlJRGFNei/nmidCdLSkSeuQd/Czdm5gzTNc2wtybUNepHonihyJc463CnczP7eQtZs+Y2BcJLfMSOOaKSOIjtC/GEnvpbAmcjQaav3BrWXR4Y7ah+2EpvrDrw+P6bwPbMux6ERNmJBezznH6uIKFuQVssK3hwExEXx3eirfnjqSuCgt1i29j8KayMl0qB9sJ50lWkbuavcffv2hfrBHWrduEIT36frfJ9LF1m3+jPm5heR/Uk58VBg3TEvlxmmp9I1WaJPeQ2FNxCt11Z8vX9JZ+7CqXe33g43s28loXauQp36w0gN8ULqP+bmF/POjncRGhvHtqSP57vRUEmI1y1t6PoU1kWDX1Ni8vElH79O1Hq2rrz78+kP9YDvoA9t6wkRYRNf/PpFjsHH7fhbk+Xj1g+1EhYVyzdkjuGVmGknxUV6XJhIwCmsiPYVzzWvRtR6ha3fdup1Qvbv9e/QZ0PEIXevRu8h4jdaJp3y7KlmU5+Ol98oIDTGuPGs4t81KZ2g/vR4gPY/Cmkhv1Fj/xQ4TnS1I3F4/2LA+nXeWaDkWM1D9YCWgNu+p4oH8Ip5fV4oZXDExmdtnZTAiQS3rpOdQWBORjjkHNXs77wPbcqxm7+HXW0jzDNfO+sC2HIuI6frfJz3Gtr0HeTC/iOcKttLY5Lh0/FDuyMogfaCWzZHuT2FNRE6O+prmyRCHWoR1MFpXtQuaGg6/PiKuk9G6Vo9h+wzQ8ibSoZ37a1i8rJin3t5MbUMTF58+lJysDE4dHOd1aSLHTWFNRLpWUxMcrPji6FzbPrAtEyjqDhx+fUhY82SIw4Jcm/ZhMUkQrpfOe6vdlbU8sryEJ1ZuoqqukS+PGcTc7EzGDuvrdWkix0xhTUSCV11Vx0Gu9ehdVTnt9oON6tcmyCW13z4sqp8mTPRQe6vreHTFJh5bUcKBmgayT0siJzuDM0f097o0kaOmsCYi3V9jQ/MM1876wLaM5LXXDzY0soPHrm0ewcYMhFAtxtod7a+p58lVm3n4rWI+q65nekYiOdkZTElL8Lo0kSNSWBOR3sO55s4RnfWBbTl2sKKdGxhEJ3TQB7ZN+7BIvSMVjKpqG3jq7c0sXlbC7spaJqcMYO65GUzPSMQ0uipBSmFNRKQ9DXWtJky0fQzbZiZsR/1g2y5A3N66ddEJWt7EAzX1jTy7ZgsPLi1mx/4axg/vx9zsDLJPS1Jok6CjsCYiciKc+7wfbEedJVqCXe2+w6+30ObHq4d1lmgn5Kkf7ElX29DIC+u2sSjfR+lnBxkzNJ652RmcP3owISEKbRIcFNZERLpK/cEOljRpE/I67Acb30kf2FYhT/1gj1l9YxP/++42FuUXUbK7ilMGxXJHVgYXnz6UUIU28ZjCmohIsGnpB/uFYNfBaF191eHXh4R/3g+2vT6wLSEvdpD6wbbR2OR45f0yFuT6KNxVSWpiDHNmp3PZhGGEh2p9P/GGwpqISHdWW3l4V4n2ZsJW76Hd5U369G8T5NqZNBE3qNf1g21qcry+YQfzc318tH0/wwf04fZZGXx94jAiw/SOoXStoAlrZnYB8D9AKPCwc+7XHZx3BfAX4CznXIF/30+A7wKNwJ3Oudc7+y6FNRHpdRrrm9ej62hJk9Yhr7H28OvDojrvA9tyLGYghIZ1/e8LEOccuR/vYl6uj/e27mVI3yhunZnGlZNHEBWu0CZdIyjCmpmFAp8C5wGlwFrgKufcR23OiwP+DkQAOc65AjMbDTwDTAaGAm8ApzjnGjv6PoU1EZEOOAc1+9pZ0qSdd+0OftbODQxiEjvvA9vyiew+fTudcyz37Wb+mz7WbKpgYFwkt8xI4+qzRxAT2XPCqQSnYwlrgfxP42TA55wr9hf1LHAp8FGb834J3Af8sNW+S4FnnXO1QImZ+fz3WxXAekVEeiYz6NOv+TPw1M7Pbaj1B7ddHbcPK//Yv7xJe/1gYzvpA9uqfVh0guf9YM2MGZkDmZE5kNXFe5ifW8ivXt3IonwfN81I49tTRxIXpQWTxXuBDGvDgK2ttkuBs1ufYGYTgOHOuVfM7Idtrl3d5tphbb/AzG4BbgEYMWLESSpbRKQXC4uEfiOaP51pavIvb7Lji0GudfuwHR9A5ZvNixa3ZaGfT4g4UvuwLugHOyUtgSlpCazb/BkLcgv57euf8NDSIq6flsqN01LoF61JG+KdQIa19t5aPfTM1cxCgPuB64/12kM7nFsMLIbmx6DHVaWIiBy7kBCISWj+DBrT+bkt/WA76gN7oAy2r29+/6695U2i+nbeB7bl2ElY3mTiyP48dsNkPijdx4K8Qua9Wcijy0u4bupIvjs9lcTYyBO6v8jxCGRYKwWGt9pOBspabccBY4F8/8rSg4GXzeySo7hWRES6i4gYGJDW/OlMUyNU7W4zQtdmtK50bXPAazh4+PWhER0Hubbr2B2hH+y45L48dN0kPt6xnwW5Ph5cWsRjK0q45uyR3DozjaT4wI/2ibQI5ASDMJonGJwLbKN5gsHVzrkNHZyfD/zQP8FgDPA0n08weBPI1AQDERFp7gd7oPM+sC3b1Xvav0dLP9gOFyRumTARB2YUlVeyMM/HS+vLCA0xrjxrOLfOSmdYP3WgkOMTFBMMnHMNZpYDvE7z0h2POuc2mNm9QIFz7uVOrt1gZktonozQANzRWVATEZFexAyi4ps/iZmdn9tQ51/epJ0g1zITdo+v+Z+NdYdfHx4NsUmkxw7m97FJ/HxSAqt2hpG/1vi/a/tzxmmn8LWZk0hOHq5+sBIwWhRXRETkUD/YjvrAtgp5NYf3g20ihKboRMLiB7XfWaL16F1EtAc/UIJNUIysiYiIdBtmED2g+ZN0Wufn1h/0B7ed7N1Vyqr3NlCyqYQBByoYawdJb9xBn50fNp/T3kOhyPhO+sC2Cnl9+nu+vIkEB42siYiInKA9lbU8vLyEJ1ZuoqqukfNHD2JuVjrj+jd03ge25e+6ysNvGhLuD3Ftg1zbd+0GNS+5It1KUHQw6GoKayIi4rW91XU8tmITj60oYX9NA1mnDiQnO5OJI/t3fmFLP9iO+sC2PIKt2k3H/WDbzoRt+0h2UPMyKL2oH2wwU1gTERHx0P6aep5ctZlHlpdQUVXHtIwE5mZnMiUt4cRu3NjweT/YI7UPa6g5/PqwqDaLEbe3bt0giEnqUf1gg5HCmoiISBCormvgqdVbeGhZMbsra5mcMoCc7AxmZCZigRzhOtQPtu2SJu20D+u0H2x7o3Vtgl436gcbTBTWREREgkhNfSPPrd3Kg0uL2L6vhjOG9+PO7AyyT0sKbGg7Gg21/lDXdoSu7XInO6Gp/vDrw2M67wPbciw6URMmWlFYExERCUK1DY28sG4bi/J9lH52kNFD4pmbncGXxwwmJCTI3yVraoKave13lmg7etdRP9iYgZ33gW0JduE9f7FhhTUREZEgVt/YxEvry1iU56N4dxWZSbHkZGdw8elDCQ320HY06qo/7wfbWfuwql3t94ON7NvJaF2rkHcS+sF6RWFNRESkG2hscvz9g+0syC3k052VpCbGMGd2OpdNGEZ4aC94ZNjU2NwSrPX7dB2N1tVXH359SPgR+sD6j8UkQVhE1/++TiisiYiIdCNNTY5/frSD+bk+NpTtJ7l/H26fnc4VE5OJDFMbK5xrXovu0GzXdiZNtByr3t3+PfoM6LwPbMuxyPguGa1TWBMREemGnHPkfbKLeW/6WL91L0P6RnHrzDSunDyCqHCFtqPSWN+8vEl7fWDbzoRtrx9sWB847SK44tGAlqmwJiIi0o0551ju2838N32s2VRBYmwkt8xM5ZqzRxITqfXPTgrn/BMm2ukD228knH1LQL9eYU1ERKSHeLt4D/NzfSz37aZ/dDjfnZ7Kt89JIT4q3OvS5AQorImIiPQw72z5jAW5PnI/3kV8VBjXT0vlxmkp9IsOrhfn5egorImIiPRQH27bx/zcQl7fsJOYiFCum5rCTTNSSYxVM/fuRGFNRESkh/tkxwEW5Pl45f0yIsNCuObskdwyM41B8VFelyZHQWFNRESklygqr2RRXhH/u34boSHGtyYN57bZ6Qzr1/O7AHRnCmsiIiK9zJY91Tyw1Mfz60pxDr5+ZjJzstIZmRDjdWnSDoU1ERGRXqps70EeWlrEM2u30tjkuPSMoczJyiAjKdbr0qQVhTUREZFebtf+Gv74VjF/Xr2FmoZGLho3hLnZGZw2ON7r0gSFNREREfHbU1nLI8tLeGLVZiprGzhv9CDuzM5kXHJfr0vr1RTWRERE5Av2Vtfxp5WbeHR5CftrGph96kDmZmcycWR/r0vrlRTWREREpF0Haup5YtVmHlleQkVVHeekJzA3O5MpaQOwLmhgLs0U1kRERKRT1XUNPP32Fh5aVkz5gVrOSulPTnYmMzMTFdq6gMKaiIiIHJWa+kaWFGzlwfwiyvbVcMbwfszNyuDcUUkKbQF0LGEtJMCFXGBmn5iZz8zuaef4bWb2gZmtN7PlZjbavz/CzB7zH3vPzGYHsk4REZHeKio8lG9PTSH/R1n819fGUVFVy01PFHDRvOW8+sF2mpp6xqBOdxawkTUzCwU+Bc4DSoG1wFXOuY9anRPvnNvv//sSYI5z7gIzuwOY5Jy7wcySgNeAs5xzTR19n0bWRERETlx9YxMvry9jYZ6P4t1VZCbFkpOdwVfGDSEsNKBjPL1KsIysTQZ8zrli51wd8CxwaesTWoKaXwzQkhxHA2/6z9kF7AWO6geJiIjI8QsPDeHrE5P51w9mMe+qCZjB955dz5d+v5QlBVupb+xw3EQCJJBhbRiwtdV2qX/fF5jZHWZWBNwH3Onf/R5wqZmFmVkqMBEY3s61t5hZgZkVlJeXn/QfICIi0luFhhiXnDGUf3xvJg9eO5GYyDB+/Pz7ZP0un6fe3kxtQ6PXJfYagQxr7b2VeNgzV+fcQudcOnA38B/+3Y/SHO4KgD8AK4GGdq5d7Jyb5JybNHDgwJNWuIiIiDQLCTEuGDuYV+ZO59HrJ5EYG8m/v/ghs+7L57EVJRysU2gLtLAA3ruUL46GJQNlnZz/LPAAgHOuAfh+ywEzWwkUBqBGEREROQpmRvZpg8g6NYkVvj3Myy3kF3/7iIV5Pm6ekca1U0YSExnIWNF7BXJkbS2QaWapZhYBXAm83PoEM8tstfkV/IHMzKLNLMb/93lAQ+uJCSIiIuINM2N6ZiJLbp3Kc7dMYdSQeP7rtY+Z/ptcFuQWsr+m3usSe5yARWDnXIOZ5QCvA6HAo865DWZ2L1DgnHsZyDGzLwH1wGfAd/yXJwGvm1kTsA24LlB1ioiIyPE5Oy2Bs9MSeGfLZyzM9fG7f37KQ8uKueGcFG6cnkq/6AivS+wRtCiuiIiInBQfbtvHglwf/9iwg5iIUK6dOpKbZ6SRGBvpdWlBRx0MRERExDOf7jzAglwfr7xfRkRYCFdPHsmts9IYFB/ldWlBQ2FNREREPFdcXsmi/CJefHcboWZ886xkbpuVTnL/aK9L85zCmoiIiASNrRXVLMov4vl1W3EOvnbmMObMziAlMcbr0jyjsCYiIiJBp2zvQRYvK+aZNVuob2zi0vHDuCMrnYykOK9L63IKayIiIhK0dh2o4Y/Livnz6i3UNDRy0dgh5GRnMGpIvNeldRmFNREREQl6eypreXRFCY+v3ExlbQPnjR7E3OwMTk/u53VpAaewJiIiIt3Gvup6HltZwqPLS9hf08CsUwZy57kZTBw5wOvSAkZhTURERLqdAzX1PLl6Mw+/VUJFVR1T0xKYe24GU9MSMGuv5Xj3pbAmIiIi3VZ1XQNPv72FxcuK2XWglkkj+zP33ExmZib2mNCmsCYiIiLdXk19I0sKtvJgfhFl+2o4I7kvOdmZfGlUUrcPbQprIiIi0mPUNTTx13dKWZRfxJaKakYNiScnK4MLxw4mJKR7hjaFNREREelxGhqbeGl9GQvzfRSXV5GRFEtOVgYXnz6EsNAQr8s7JgprIiIi0mM1Njle/WA7C3J9fLLzACkJ0cyZncHlZw4jvJuENoU1ERER6fGamhz/2riT+bmFfLhtP8P69eH22el8Y1IykWGhXpfXKYU1ERER6TWcc+R/Us683ELe3bKXQfGR3Doznasmj6BPRHCGNoU1ERER6XWcc6ws2sO8Nwt5u6SCxNgIbpqRxrVTRhIbGeZ1eV+gsCYiIiK92pqSCubnFvJW4W76RYfz3WmpfGdaCvFR4V6XBiisiYiIiADw7pbPWJjn442Nu4iLCuP6c1K4cVoq/WMiPK1LYU1ERESklQ1l+1iQ6+O1D3cQExHKtVNHctP0NAbGRXpSj8KaiIiISDs+3XmAhXk+/vZeGRFhIVw1eQS3zkxncN+oLq1DYU1ERESkE8XllSzKL+LFd7cRasY3JiVz++x0kvtHd8n3K6yJiIiIHIWtFdU8sLSIvxRsxTn42pnDmDM7g5TEmIB+77GEte6xzK+IiIhIAAwfEM3/u3wcy36cxbVTRvLS+jJ+/Pz7Xpf1BcG16IiIiIiIB4b07cPPLxnDnKx09lbXe13OFyisiYiIiPglxUWRFNe1kw2OJKCPQc3sAjP7xMx8ZnZPO8dvM7MPzGy9mS03s9H+/eFm9rj/2EYz+0kg6xQREREJVgELa2YWCiwELgRGA1e1hLFWnnbOjXPOjQfuA37v3/8NINI5Nw6YCNxqZimBqlVEREQkWAVyZG0y4HPOFTvn6oBngUtbn+Cc299qMwZomZrqgBgzCwP6AHVA63NFREREeoVAvrM2DNjaarsUOLvtSWZ2B/ADIALI9u9+nuZgtx2IBr7vnKsIYK0iIiIiQSmQI2vWzr7DFnVzzi10zqUDdwP/4d89GWgEhgKpwF1mlnbYF5jdYmYFZlZQXl5+8ioXERERCRKBDGulwPBW28lAWSfnPwtc5v/7auAfzrn/396dx8pZ1WEc/z6hqEgFlMVUFkugYFFoq6hE2SwYBKPGRNwIGDSBsAlRCcQ/0AiJNXEholAalgoYwECDmAhijAJlkUIpty0VqBChyCKyiiDc9vGP91Qn7dy5Q+mdOcw8n2SSd94575nfnV/mvr8573Jetf0kcAuw3o3jbM+zvbftvbfddtuNFHZEREREPSayWFsETJO0s6Q3AV8Erm1tIGlay9NPAg+U5YeB2WpsDuwD/GUCY42IiIio0oSds2Z7VNKJwO+ATYCLbC+X9D3gTtvXAidKOhh4FXgG+ErZ/OfAxcAymsOpF9uu63bCERERET2QuUEjIiIieixzg0ZEREQMiIEZWZP0D+BvPXirbYCnevA+0b3kpE7JS32SkzolL/XpRU7ebburqyMHpljrFUl3djtsGb2RnNQpealPclKn5KU+teUkh0EjIiIiKpZiLSIiIqJiKdZeu3n9DiDWk5zUKXmpT3JSp+SlPlXlJOesRURERFQsI2sRERERFUux1oakiyQ9KWnZGK9L0k8lrZQ0Iun9vY5x2HSRkyNKLkYk3SppRq9jHEbj5aWl3QclrZb0uV7FNqy6yYmkAyUtkbRc0o29jG9YdfE/bEtJv5F0T8nL0b2OcdhI2lHSHyWtKJ/5yW3aVLG/T7HW3nzgEx1ePxSYVh7HAOf1IKZhN5/OOXkIOMD2XsCZVHa+wQCbT+e8IGkT4Ac0U8/FxJtPh5xI2go4F/i07fcCh/cormE3n87flROAe23PAA4EflTm1Y6JMwp80/Z0mjnIT5C0xzptqtjfp1hrw/ZNwNMdmnwGuMSN24GtJE3pTXTDabyc2K3vSFoAAAVeSURBVL7V9jPl6e3ADj0JbMh18V0BOAm4Gnhy4iOKLnLyZWCB7YdL++SlB7rIi4G3SRIwubQd7UVsw8r2Y7YXl+UXgBXA9us0q2J/n2Jtw2wPPNLyfBXrJzj652vAdf0OIkDS9sBngbn9jiX+Zzfg7ZL+JOkuSUf1O6AA4GfAdODvwFLgZNtr+hvS8JA0FZgF/Hmdl6rY30/q9RsOCLVZl8tqKyDpYzTF2r79jiUAOBs4zfbqZsAgKjAJ+ABwELAZcJuk223f39+wht4hwBJgNrAL8HtJN9t+vr9hDT5Jk2lG/09p83lXsb9PsbZhVgE7tjzfgebXUPSRpL2AC4BDbf+z3/EEAHsDV5RCbRvgMEmjtq/pb1hDbRXwlO0XgRcl3QTMAFKs9dfRwBw399NaKekh4D3AHf0Na7BJ2pSmUPul7QVtmlSxv89h0A1zLXBUuUpkH+A524/1O6hhJmknYAFwZEYI6mF7Z9tTbU8FrgKOT6HWd78G9pM0SdJbgQ/TnKsT/fUwzWgnkt4J7A482NeIBlw5P/BCYIXtH4/RrIr9fUbW2pB0Oc3VONtIWgV8B9gUwPZc4LfAYcBK4N80v4hiAnWRkzOArYFzyyjOaE2T8A6qLvISPTZeTmyvkHQ9MAKsAS6w3fHWK/H6dfFdOROYL2kpzaG302w/1adwh8VHgSOBpZKWlHXfBnaCuvb3mcEgIiIiomI5DBoRERFRsRRrERERERVLsRYRERFRsRRrERERERVLsRYRERFRsRRrEdFXkraWtKQ8Hpf0aMvzriaylnSxpN3HaXOCpCM2UswLJd3XEueVG6Pflv5XlQnXIyJy646IqIek7wL/sv3DddaL5v9VFXMlSloInGh7ybiNN6z/VcD7bD87Ef1HxBtLRtYiokqSdpW0TNJcYDEwRdI8SXdKWi7pjJa2CyXNLHflf1bSHEn3SLpN0nalzVmSTmlpP0fSHWWE7CNl/eaSri7bXl7ea+ZriPkySedJulnS/ZIOLes3k/QLSUslLZa0f1k/SdJPyt85Iun4lu5OkXR3Wb9baT+7xLak9LP56/yYI+INIMVaRNRsD+BC27NsPwqcXmammAF8XNIebbbZErjR9gzgNuCrY/Qt2x8CTqWZAQPgJODxsu0cYFaH2K5sOQw6p2X9jsABwKeAeZLeDHwdeMX2njR3TL+0HOI9DngXMMP2XsAVLf08YXsWzXy33yjrTgWOsT0T2B94uUN8ETEgUqxFRM3+antRy/MvSVpMM9I2naaYW9dLtq8ry3cBU8foe0GbNvtSCibb9wDLO8T2Bdszy+P0lvW/sr3G9n3AI8C00u+lpd/lNBNB7wocDMy1vbq89vQ48d0CnC3pJGCLtdtFxGBLsRYRNXtx7YKkacDJwOwyCnU98JY227zSsryasedA/k+bNnpd0TbWPRHYHfpVm/ZrrRef7bOAY4HJwKLymUTEgEuxFhFvFFsALwDPS5oCHDIB77EQ+DyApD1pP3I3nsPV2I3mkOgDwE3AEaXf6cAUmomhbwCOk7RJee0dnTqWtIvtEdvfB+4GOl4BGxGDYaxfnBERtVkM3AssAx6kOSS4sZ0DXCJppLzfMuC5MdpeKemlsvyE7bXF40qa4mw7mvPLXpF0DnC+pKXAq8BRZf35NIdJRySNAucBczvE9y1J+wFrgBGaYi8iBlxu3RERUUiaBEyy/XI5xHgDMM32aJfbXwZcZfuaiYwzIoZLRtYiIv5vMvCHUrQJOLbbQi0iYqJkZC0iIiKiYrnAICIiIqJiKdYiIiIiKpZiLSIiIqJiKdYiIiIiKpZiLSIiIqJiKdYiIiIiKvZf+emTzSnfYWIAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Train-Validation Progress\n",
    "num_epochs=params_train[\"num_epochs\"]\n",
    "\n",
    "# plot loss progress\n",
    "plt.title(\"Train-Val Loss\")\n",
    "plt.plot(range(1,num_epochs+1),loss_hist[\"train\"],label=\"train\")\n",
    "plt.plot(range(1,num_epochs+1),loss_hist[\"val\"],label=\"val\")\n",
    "plt.ylabel(\"Loss\")\n",
    "plt.xlabel(\"Training Epochs\")\n",
    "plt.legend()\n",
    "plt.show()\n",
    "\n",
    "# plot accuracy progress\n",
    "plt.title(\"Train-Val Accuracy\")\n",
    "plt.plot(range(1,num_epochs+1),metric_hist[\"train\"],label=\"train\")\n",
    "plt.plot(range(1,num_epochs+1),metric_hist[\"val\"],label=\"val\")\n",
    "plt.ylabel(\"Accuracy\")\n",
    "plt.xlabel(\"Training Epochs\")\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
